We employ the VCTK Corpus for our experiments [1]}. We select 80 speakers for training and 14 for test. We trim large silence regions to 100 ms with the help of a voice activity detector. After this, we get roughly 20 h of training speech and 3 h of test speech. We incorporate the aforementioned distortions (Sec. ) with an online process jointly working with training. When we construct a training minibatch, we (1) load the clean utterance, (2) get a random \(\approx \) 1 s chunk (16384 samples) inside the utterance, and (3) apply a series of transforms that get activated independently under a probability \(p=0.4\) . This way one or two distortions coincide often, and zero (thus purely autoencoder) or more than two coincide less. Table REF  shows this activation distribution with up to the 4 mentioned combinations. In addition to being active or not, each transform has a certain severity level or factor, as mentioned before (sec. ). The only transform with just one level of severity is the whispering transform. Hence every time a transformation is activated in the pipeline, one of the possible factors is randomly selected. These possibilities are: clip factors of 30%, 40% and 50%, signal resampling factors of 2, 4, and 8 and, in the case of chunk removal, we allow the system to zero out up to 5 chunks from speech regions. Then, inside each region, the length of the chunk is sampled from one of the two mentioned Gaussian distributions (sec. ).
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